template.docI.J. Information Engineering and Electronic Business, 2013, 6, 22-32 Published Online December 2013 in MECS (http://www.mecs-press.org/)
DOI: 10.5815/ijieeb.2013.06.03
Copyright © 2013 MECS I.J. Information Engineering and Electronic Business, 2013, 6, 22-32
Design High Efficiency Intelligent Robust Back
stepping Controller
Kamran Heidari, Farzin Piltan, Samaneh Zahmatkesh, Sara Heidari, Mahdi Jafari
Institute of Advance Science and Technology, Intelligent control and Robotics Lab. IRAN SSP, Shiraz/Iran,
http://WWW.IRANSSP.COM
freedom (DOF) continuum robot in presence of highly
nonlinear dynamic parameters in a number of industries
has motivated a flurry of research in the development of
soft computing nonlinear methodology. This research
contributes to the on-going research effort by exploring
alternate methods for controlling the continuum robot
manipulator. This research addresses two basic issues
related to the control of a continuum robots; (1) a more
accurate representation of the dynamic model of an
existing prototype, and (2) the design of a robust
feedback controller. The robust back stepping controller
proposed in this research is used to further demonstrate
the appealing features exhibited by the continuum robot.
Robust feedback controller is used to position control of
continuum robot in presence of uncertainties. Using
Lyapunov type stability arguments, a robust back
stepping controller is designed to achieve this objective.
The controller developed in this research is designed into
two steps. Firstly, a robust stabilizing torque is designed
for the nominal continuum robot dynamics derived using
the constrained Lagrangian formulation. Next, the fuzzy
logic methodology applied to it to solution uncertainty
problem. The fuzzy model free problem is formulated to
minimize the nonlinear formulation of continuum robot.
The eventual stability of the controller depends on the
torque generating capabilities of the continuum robots.
Index Term — Back stepping control methodology,
fuzzy inference system, continuum robot manipulator,
robust control.
1. Introduction
appealing features exhibited by the spherical motor, but
also demonstrates some of the nice features of Back
stepping-type controllers as well. The work done in this
research contributes to bringing the continuum robot a
step closer to practical industrial application. Controller is
a device which can sense information from linear or
nonlinear system (e.g., continuum robot) to improve the
systems performance [1-5]. The main targets in designing
control systems are stability, good disturbance rejection,
and small tracking error[6-12]. Several continuum robot
are controlled by linear methodologies (e.g.,
Proportional-Derivative (PD) controller, Proportional-
Derivative (PID) controller), but when robot works with
various payloads and have uncertainty in dynamic models
this technique has limitations. In some applications
continuum robot are used in an unknown and
unstructured environment, therefore strong mathematical
tools used in new control methodologies to design
nonlinear robust controller with an acceptable
performance (e.g., minimum error, good trajectory,
disturbance rejection) [12-15].
been applied to the control of numerous single­ axis
machines and robotic manipulators. Since available
control techniques for continuum robot and robotic
manipulators are so broad, the review in this research is
restricted to some of the nonlinear control techniques for
continuum robot. Most of the authors referenced on the
nonlinear control techniques for continuum robot also do
a significant amount of work on the control of robotic
manipulators. Kokotovic [14-16] published one of the
pioneering works on the back stepping control technique
and Qu et al. [14] extended this technique and developed
a robust back stepping-type controller for a one-link robot
with the motor dynamics taken into consideration. Carroll
et al. [15] also extended the work of Kokotovic [16] to
design an embedded computed torque and output
feedback controller for penitent magnet brush de (BDC)
motors. Hematite et al. [16] developed a robust feedback
linear zing controller for a single-link robot actuated by a
brushless de motor (BLOC). In [17], Carroll et al. also
developed a robust tracking controller for a BLOC, which
achieved globally bounded results for rotor position
tracking error despite parametric uncertainties and
additive bounded disturbances. In addition to DC
machines, SR and PM stepper motors are also candidates
for advanced nonlinear controllers. In [18], Die'-Spong et
al. introduced a detailed nonlinear model and an
electronic commutation strategy for the SR motor and
applied a state feedback control algorithm which
compensated for all the nonlinearities of the system. The
work in [18] was then generalized to a direct-drive
manipulator with SR actuation by Taylor et al. [19].
Carroll et al. [20] also used a back stepping technique to
develop an adaptive tracking controller for the SR motor.
Bodson [21] developed a model-based control law for the
PM stepper motor using an exact linearization
methodology while considering practical issues such as
Copyright © 2013 MECS I.J. Information Engineering and Electronic Business, 2013, 6, 22-32
voltage saturation. Even though some of the above
control techniques are not of the back stepping type
controller, the back stepping-type controller developed in
this thesis was somewhat inspired by them.
In recent years, artificial intelligence theory has been
used in nonlinear control systems. Neural network, fuzzy
logic, and neuro-fuzzy are synergically combined with
nonlinear classical controller and used in nonlinear, time
variant, and uncertainty plant (e.g., robot manipulator).
Fuzzy logic controller (FLC) is one of the most important
applications of fuzzy logic theory. This controller can be
used to control nonlinear, uncertain, and noisy systems.
This method is free of some model-based techniques as in
classical controllers. As mentioned that fuzzy logic
application is not only limited to the modeling of
nonlinear systems [22-37]but also this method can help
engineers to design easier controller. Control robot arm
manipulators using classical controllers are based on
manipulator dynamic model. These controllers often have
many problems for modelling. Conventional controllers
require accurate information of dynamic model of robot
manipulator, but these models are multi-input, multi-
output and non-linear and calculate accurate model can
be very difficult. When the system model is unknown or
when it is known but complicated, it is difficult or
impossible to use classical mathematics to process this
model[32]. The main reasons to use fuzzy logic
technology are able to give approximate recommended
solution for unclear and complicated systems to easy
understanding and flexible. Fuzzy logic provides a
method which is able to model a controller for nonlinear
plant with a set of IF-THEN rules, or it can identify the
control actions and describe them by using fuzzy rules. It
should be mentioned that application of fuzzy logic is not
limited to a system thats difficult for modeling, but it can
be used in clear systems that have complicated
mathematics models because most of the time it can be
shortened in design but there is no high quality design
just sometimes we can find design with high quality.
Besides using fuzzy logic in the main controller of a
control loop, it can be used to design adaptive control,
tuning parameters, working in a parallel with the classical
and non classical control method [32-39]. The
applications of artificial intelligence such as neural
networks and fuzzy logic in modelling and control are
significantly growing especially in recent years. For
instance, the applications of artificial intelligence, neural
networks and fuzzy logic, on robot arm control have
reported in [40-57].
A serial link continuum robot is a sequence of joints
and links which begins with a base frame and ends with
an end-effector. This type of robot manipulators,
comparing with the load capacitance is more weightily
because each link must be supported the weights of all
next links and actuators between the present link and end-
effector[12-18]. Serial continuum robot manipulators
have been used in medical application, and also in
research laboratories. One of the most important
classifications in controlling the robot manipulator is how
the links have connected to the actuators. This
classification divides into two main groups: highly geared
(e.g., 200 to 1) and direct drive (e.g., 1 to 1). High gear
ratios reduce the nonlinear coupling dynamic parameters
in robot manipulator. In this case, each joint is modeled
the same as SISO systems. In high gear robot
manipulators which generally are used in industry, the
couplings are modeled as a disturbance for SISO systems.
Direct drive increases the coupling of nonlinear dynamic
parameters of robot manipulators. This effect should be
considered in the design of control systems. As a result
some control and robotic researchers works on nonlinear
robust controller design[19-24]. Most of continuum robot
manipulator is high gear, so this research focuses on
design SISO controller.
This paper is organized as follows; section 2, is served
as an introduction to the feedback back stepping
controller formulation algorithm and its application to
control of continuum robot and dynamic of continuum
robot. Part 3, introduces and describes the methodology.
Section 4 presents the simulation results and discussion
of this algorithm applied to a continuum robot and the
final section is describing the conclusion.
2. Theory
consists of three modules stacked together in series. In
general, the model will be a more precise replication of
the behavior of a continuum arm with a greater of
modules included in series. However, we will show that
three modules effectively represent the dynamic behavior
of the hardware, so more complex models are not
motivated. Thus, the constant curvature bend exhibited by
the section is incorporated inherently within the model.
The model resulting from the application of Lagranges
equations of motion obtained for this system can be
represented in the form
(1)
where is a vector of input forces and q is a vector of
generalized co-ordinates. The force coefficient matrix
transforms the input forces to the generalized forces
and torques in the system. The inertia matrix, is
composed of four block matrices. The block matrices that
correspond to pure linear accelerations and pure angular
accelerations in the system (on the top left and on the
bottom right) are symmetric. The matrix contains
coefficients of the first order derivatives of the
generalized co-ordinates. Since the system is nonlinear,
many elements of contain first order derivatives of the
generalized co-ordinates. The remaining terms in the
dynamic equations resulting from gravitational potential
energies and spring energies are collected in the matrix .
The coefficient matrices of the dynamic equations are
given below,
[ ( ) ( ) ( ) ( ) ( ) ( ) ⁄ ⁄ ⁄ ⁄ ⁄ ( ) ⁄ ( ) ⁄ ⁄ ⁄ ⁄ ⁄ ⁄ ]
dynamics in Equation (1) have the appropriate structure
for the so-called back stepping controller design method.
The steps in the back stepping controller design method
are outlined in Figure 2. With the position error defined
as , all joints will track the desired
specified state if the error dynamics are given as
follows:
( , - ) (6)
dynamics in Equation (6) can be rewritten as:
, - (7)
the position error dynamics go to zero. Since the state
vector is not a control variable, Equation (7) cannot
be directly substituted into Equation (1). The
expression in Equation (7) is therefore defined as a
fictitious control input and is labeled expressed below
as .
, -( ) (8)
as the specified velocity trajectory and hence the
velocity error can be defined as .
With the following dynamics
of the joint position error. The error dynamics in equation
(9) can be rewritten as:
, - (10)
the following expression as the desired stabilizing torque:
, -( , - ) ( ) (11)
compensator since it depends on the dynamics of the
spherical motor.The time derivative of desired velocity
vector is calculated using Equation (9). In terms of the
desired state trajectory, and its time derivatives and the
position and velocity state variables, the desired torque
can be rewritten in following form:
, - ( ) (12)
Where
similar to inverse dynamics control algorithm developed
for robotic manipulators. The back stepping controller is
ideal from a control point of view as the nonlinear
dynamics of the continuum robot are cancelled and
replaced by linear subsystems. The drawback of the back
stepping controller is that it requires perfect cancellation
of the nonlinear continuum robot dynamics. Accurate
real time representations of the robot dynamics are
difficulty due to uncertainties in the system dynamics
resulting from imperfect knowledge of the robot
mechanical parameters; existence of unmolded dynamics
and dynamic uncertainties due to payloads. The
requirement for perfect dynamic cancellation raises
sensitivity and robustness issues that are addressed in the
design of a robust back stepping controller. Another
drawback of the back stepping controller is felt during
real-time implementation of the control algorithm.
Implementation of the back stepping controller requires
Design High Efficiency Intelligent Robust Back stepping Controller 25
Copyright © 2013 MECS I.J. Information Engineering and Electronic Business, 2013, 6, 22-32
the computation of the exact robot dynamics at each
sampling time. This computational burden has an effect
on the performance of the control algorithm and imposes
constraints on the hardware/software architecture of the
control system. By only computing the dominant parts of
the robot dynamics, this computational burden can be
reduced. These drawbacks of the back stepping controller
makes it necessary to consider control algorithms that
compensate for both model uncertainties and for
approximations made during the on-line computation of
robot dynamics. The next section provides robust
modifications of the back stepping controller described
in this section. Figure 1 shows the block diagram of back
stepping controller
Robust Back stepping Control: When there are
uncertainties in the spherical motor dynamics due to
modeling inaccuracy and computational relaxation,
robust controllers are ideal for ensuring system stability.
When the system dynamics are completely known, the
required torque control vector for the control of the
spherical motor is given by Equations (12) and (13). In
the presence of modeling uncertainties, a reasonable
approximation of the torque control input vector is given
by
[ ] (14)
terms in the spherical motor dynamics; and is given by
Equation (13). The uncertainty on the estimates are
expressed as
modeling and intentional computational simplification.
Application of the approximate control vector given by
Equations (14) leads to the following expression for the
closed loop dynamics:
be rewritten as
results in the following expression for the closed loop
error dynamics.
[ ]
the error dynamics in Equation (20) can be expressed as
, - , -( ) (22)
Where , - [ , - , - , - , -
Since is a nonlinear function of the position and
velocity state vectors, the system error dynamics in the
above equation are nonlinear and coupled. The back
stepping controller developed in the previous section
cannot guarantee system stability. The Lyapunov direct
method is, however, used to design an outer feedback
loop on the error dynamics that compensates for the
system uncertainty contributed by .
review about foundation of fuzzy logic based on [15-20].
Supposed that is the universe of discourse and is the
element of , therefore, a crisp set can be defined as a set
which consists of different elements ( ) will all or no
membership in a set. A fuzzy set is a set that each
element has a membership grade, therefore it can be
written by the following definition;
* ( )| + (23)
the membership function (MF) of fuzzy set. The
membership function ( ( )) of fuzzy set must have a
value between zero and one. If the membership function
( ) value equal to zero or one, this set change to a
crisp set but if it has a value between zero and one, it is a
fuzzy set. Defining membership function for fuzzy sets
has divided into two main groups; namely; numerical and
functional method, which in numerical method each
number has different degrees of membership function and
functional method used standard functions in fuzzy sets.
The membership function which is often used in practical
applications includes triangular form, trapezoidal form,
bell-shaped form, and Gaussian form.
Linguistic variable can open a wide area to use of
fuzzy logic theory in many applications (e.g., control and
26 Design High Efficiency Intelligent Robust Back stepping Controller
Copyright © 2013 MECS I.J. Information Engineering and Electronic Business, 2013, 6, 22-32
system identification). In a natural artificial language all
numbers replaced by words or sentences.
Rule statements are used to formulate the
condition statements in fuzzy logic. A single fuzzy
rule can be written by
(24)
Where and are the Linguistic values that can be
defined by fuzzy set, the of the part of
is called the antecedent part and the of the part of is called the Consequent or
Conclusion part. The antecedent of a fuzzy if-then rule
can have multiple parts, which the following rules shows
the multiple antecedent rules:
Big, is Medium Left, is torque and is Large Left.
rules have three parts, namely, fuzzify inputs,
apply fuzzy operator and apply implication method which
in fuzzify inputs the fuzzy statements in the antecedent
replaced by the degree of membership, apply fuzzy
operator used when the antecedent has multiple parts and
replaced by single number between 0 to 1, this part is a
degree of support for the fuzzy rule, and apply
implication method used in consequent of fuzzy rule to
replaced by the degree of membership. The fuzzy
inference engine offers a mechanism for transferring the
rule base in fuzzy set which it is divided into two most
important methods, namely, Mamdani method and
Sugeno method. Mamdani method is one of the common
fuzzy inference systems and he designed one of the first
fuzzy controllers to control of system engine. Mamdanis
fuzzy inference system is divided into four major steps:
fuzzification, rule evaluation, aggregation of the rule
outputs and defuzzification. Michio Sugeno uses a
singleton as a membership function of the rule
consequent part. The following definition shows the
Mamdani and Sugeno fuzzy rule base
( )
calculates the membership degrees for antecedent part.
Rule evaluation focuses on fuzzy operation ( )
in the antecedent of the fuzzy rules. The aggregation is
used to calculate the output fuzzy set and several
methodologies can be used in fuzzy logic controller
aggregation, namely, Max-Min aggregation, Sum-Min
aggregation, Max-bounded product, Max-drastic product,
Max-bounded sum, Max-algebraic sum and Min-max.
Two most common methods that used in fuzzy logic
controllers are Max-min aggregation and Sum-min
aggregation. Max-min aggregation defined as below.
( )
( )
( )
( )
and and also
( ) is a fuzzy
interpretation of rule. Defuzzification is the last
step in the fuzzy inference system which it is used to
transform fuzzy set to crisp set. Consequently
defuzzifications input is the aggregate output and the
defuzzifications output is a crisp number. Centre of
gravity method ( ) and Centre of area method ( ) are two most common defuzzification methods, which
method used the following equation to calculate the
defuzzification
the defuzzification.
element of an output of the fuzzy set, ( ) is
the fuzzy set membership function, and is the number
of fuzzy rules.
fuzzy logic controller has played important rule to design
nonlinear controller for nonlinear and uncertain systems
[20-32]. However the application area for fuzzy control is
really wide, the basic form for all command types of
controllers consists of:
Inference engine
Design High Efficiency Intelligent Robust Back stepping Controller 27
Copyright © 2013 MECS I.J. Information Engineering and Electronic Business, 2013, 6, 22-32
Figure 2. Fuzzy Inference System
3. Methodology
This step is focused on the design minimum rule base
Mamdanis back stepping fuzzy controller with
application to continuum robot manipulator. As
mentioned above pure back stepping controller has
nonlinear dynamic parameters limitation in presence of
uncertainty and external disturbances. In order to solve
this challenge fuzzy inference system is serially applied
to back stepping controller. The back stepping method is
based on mathematical formulation which this method is
introduced new variables into it in form depending on the
dynamic equation of continuum robot arm. This method
is used as feedback linearization in order to solve
nonlinearities in the system. Back stepping controller is
divided into two main parts; linear part and nonlinear
dynamic formulation.. The back stepping controller for
continuum robot is calculated by;
(31)
back stepping nonlinear equivalent function which can be
written as (32) and is backstepping control law which
calculated by (1)
Based on above formulation, FIS in this research has an
input ( ( ) and an output( ) , Figure 3 shows
fuzzy inference system.
∑
( )
controller fuzzy inference system is used in serial with
nonlinear dynamic part.
stepping fuzzy control is;
robust term that compensates for the uncertainty in the
system dynamics is added to the control law in Equations
(13) and (14). The robust control law, therefore, has the
following form:
system uncertainties. The proper choice of ensures the
stability of the system even in the presence of
uncertainties. Figure 4 shows a block diagram detailing
the steps in the robust back stepping fuzzy control of
continuum robot.
Figure 4. Block diagram of back stepping fuzzy controller for
continuum robot
(38), the system error dynamics in Equation (35) can be
rewritten in the following form:
, - , -( ) (39)
where
Copyright © 2013 MECS I.J. Information Engineering and Electronic Business, 2013, 6, 22-32
When there are no uncertainties in the system
dynamics, the robust term in the applied controller return
is identically zero and the control law returns to the back
stepping control developed in the previous section. Figure
2 shows a block diagram of the robust back stepping
controller.
robust controller are determined using Lyapunov methods.
The following positive definite quadratic function is
selected as a Lyapunov function:
, - (42)
time derivative of the Lyapunov function along the
trajectories of the error dynamics results in the following
equation:
definite matrix , - ; and a symmetric positive definite
matrix , -, the following identity holds;
, - , - , -, - , - (44)
Equation (44) can therefore be rewritten as
, - , -, -( ) (45)
negative definite if the second term in the above equation
is either negative or zero. The error dynamics converge to
zero when the time derivative of the Lyapunov function
in Equation (45) is negative definite. Selecting the robust
control term w as
results in the following expression for the time derivative
of the Lyapunov function.
( ) (48)
( ). Therefore, selecting p such that
(49)
of the Lyapunov function along the dynamics of .
With the definition of given in Equation (49), the
following bound can be put on the uncertainty term .
, - [ ]( , - , -) , - , - , -
solved to give the following relationship
( , - , - ) (51)
time derivative of the Lyapunov function is negative
, - (
)
tested to Step response trajectory. In this simulation, to
control position of continuum robot is moved from home
to final position without and with external disturbance.
The simulation was implemented in
MATLAB/SIMULINK environment. These systems are
tested by band limited white noise with a predefined 40%
of relative to the input signal amplitude. This type of
noise is used to external disturbance in continuous and
hybrid systems and applied to nonlinear dynamic of these
controllers.
performance for FBSC and BSC without disturbance.
Figure 5. FBSC and BSC for first, second and third joint
trajectory
By comparing step response trajectory without
disturbance in BSC and FBSC, it is found that the FBSC's
overshoot (1.4%) is lower than BSC's (1.6%).
Disturbance rejection: Figure 6 has shown the power
disturbance elimination in BSC and FBSC. The main
target in this controller is disturbance rejection as well as
the other responses. A band limited white noise with
predefined of 40% the power of input signal is applied to
BSC and FBSC. It found fairly fluctuations in trajectory
responses. As mentioned earlier, BSC works very well
Design High Efficiency Intelligent Robust Back stepping Controller 29
Copyright © 2013 MECS I.J. Information Engineering and Electronic Business, 2013, 6, 22-32
when all parameters are known, or we have a limitation
uncertainty in parameters.
Figure 6. FBSC and BSC for first, second and third joint
trajectory with disturbance.
fairly fluctuations. By comparing some control
parameters such as overshoot, rise time, steady state and
RMS error it is computed that the FBSC's overshoot
(1.6%) is lower than BSC's (3%). Both of them have
about the same rise time; FBSC (0.5 sec) and BSC (0.49
sec).
The dynamic parameters of this system are highly
nonlinear. To control of this system robust back stepping
methodology is introduced. Back stepping controller
(BSC) is an influential robust nonlinear controller to
certain and partly uncertain systems. When all dynamic
and physical parameters are known BSC works superbly;
practically a large amount of systems have uncertainties
and fuzzy model base BSC is used. Robust back stepping
fuzzy controller (FBSC) is a mathematical model base
method to off-line control of highly nonlinear systems
such as continuum robot. Based on this research FBSC is
more robust in presence of uncertainties and external
disturbance however it is off-line tuning methodology.
Acknowledgment
reviewers for their careful reading of this paper and for
their helpful comments. This work was supported by the
Institute of Advanced Science and Technology
(IRANSSP) Research and Development Corporation
Program of Iran under grant no. 2012-Persian Gulf-4B.
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[31] Farzin Piltan, M.H. Yarmahmoudi, M. Mirzaei, S.
Emamzadeh, Z. Hivand, “Design Novel Fuzzy
Robust Feedback Linearization Control with
Application to Robot Manipulator”, International
Journal of Intelligent Systems and Applications ,
vol.5, no.5, pp.1-10, 2013.DOI:
kamgari, S. Zare, “Evaluation Performance of IC
Engine: Linear Tunable Gain Computed Torque
Controller Vs. Sliding Mode Controller”,
International Journal of Intelligent Systems and
Applications, vol.5, no.6, pp.78-88, 2013.DOI:
10.5815/ijisa.2013.06.10.
“Colonial Competitive Optimization Sliding Mode
Controller with Application to Robot Manipulator”,
International Journal of Intelligent Systems and
Applications, vol.5, no.7, pp.50-56, 2013. DOI:
10.5815/ijisa.2013.07.07.
[34] Salehi, Farzin Piltan, M. Mousavi, A. Khajeh, M. R.
Rashidian, “Intelligent Robust Feed-forward Fuzzy
Feedback Linearization Estimation of PID Control
with Application to Continuum Robot”,
International Journal of Information Engineering
and Electronic Business, vol.5, no.1, pp.1-16, 2013.
DOI: 10.5815/ijieeb.2013.01.01.
Hosainpour, S. Soltani, “A Design High Impact
Lyapunov Fuzzy PD-Plus-Gravity Controller with
Application to Rigid Manipulator”, International
Journal of Information Engineering and Electronic
Business, vol.5, no.1, pp.17-25, 2013. DOI:
10.5815/ijieeb.2013.01.02.
M. Adibi, “Model-Free Adaptive Fuzzy Sliding
Mode Controller Optimized by Particle Swarm for
Robot manipulator”, International Journal of
Information Engineering and Electronic Business,
vol.5, no.1, pp.68-78, 2013. DOI:
10.5815/ijieeb.2013.01.08.
M. kamgari, S. Zare, “Robust Fuzzy PD Method
with Parallel Computed Fuel Ratio Estimation
Applied to Automotive Engine“, International
Design High Efficiency Intelligent Robust Back stepping Controller 31
Copyright © 2013 MECS I.J. Information Engineering and Electronic Business, 2013, 6, 22-32
Journal of Intelligent Systems and Applications,
vol.5, no.8, pp.83-92, 2013. DOI:
10.5815/ijisa.2013.08.10.
Bazregar, “Design Robust Fuzzy Sliding Mode
Control Technique for Robot Manipulator Systems
with Modeling Uncertainties”, International Journal
of Information Technology and Computer Science,
vol.5, no.8, pp.123-135, 2013. DOI:
10.5815/ijitcs.2013.08.12.
F. ShahryarZadeh “Management of Environmental
Pollution by Intelligent Control of Fuel in an
Internal Combustion Engine“ Global Journal of
Biodiversity Science And Management, 3(1), 2013.
[40] M. M. Ebrahimit Farzin Piltan, M. Bazregar and
A.R. Nabaee, “Intelligent Robust Fuzzy-Parallel
Optimization Control of a Continuum Robot
Manipulator”, International Journal of Control and
Automation, 6(3), 2013.
[41] O.R. Sadrnia, Farzin Piltan, M. Jafari, M. Eram and
M. Shamsodini, “Design PID Estimator Fuzzy plus
Backstepping to Control of Uncertain Continuum
Robot”, International Journal of Hybrid Information
Technology, 6(4), 2013.
Hasiri, M.R Hashemzadeh, “Design Novel Soft
Computing Backstepping Controller with
Systems and Applications, vol.5, no.10, pp.93-105,
2013. DOI: 10.5815/ijisa.2013.10.12.
and Farzin Piltan, “Design New Artificial
Intelligence Base Modified PID Hybrid Controller
for Highly Nonlinear System”, International Journal
of Advanced Science and Technology, 57, 2013.
[44] S. Zahmatkesh, Farzin Piltan, K. Heidari, M.
Shamsodini, S. Heidari, “Artificial Error Tuning
Based on Design a Novel SISO Fuzzy Backstepping
Adaptive Variable Structure Control” International
Journal of Intelligent Systems and Applications,
vol.5, no.11, pp.34-46, 2013.DOI:
and S. Zahmatkesh, “Design New Nonlinear
Controller with Parallel Fuzzy Inference System
Compensator to Control of Continuum Robot
Manipulator”,International Journal of Control and
Automation, 6(4), 2013.
ShahryarZadeh, M. Mansoorzadeh, “Design Novel
Model Reference Artificial Intelligence Based
Methodology to Optimized Fuel Ratio in IC
Engine”, International Journal of Information
Engineering and Electronic Business, vol.5, no.2,
pp.44-51, 2013. DOI: 10.5815/ijieeb.2013.02.07.
Omid Reza Sadrnia, Mahdi Jafari,"Nonlinear Fuzzy
Model-base Technique to Compensate Highly
Nonlinear Continuum Robot Manipulator", IJISA,
vol.5, no.12, pp.135-148, 2013. DOI:
10.5815/ijisa.2013.12.12.
Hashemzadeh, Fatemeh BibakVaravi, Hossein
Hashemzadeh,"Design Parallel Linear PD
2013. DOI: 10.5815/ijitcs.2013.12.12.
Nazari, M. Mirzaie, “Design Sliding Mode
Controller of with Parallel Fuzzy Inference System
Compensator to Control of Robot Manipulator”,
International Journal of Robotics and Automation,
Vol. 2, No. 4, December 2013, pp. 149~162.
[50] Farzin Piltan, Mahdi Jafari, Mehdi Eram, Omid
Mahmoudi, Omid Reza Sadrnia, "Design Artificial
Intelligence-Based Switching PD plus Gravity for
Highly Nonlinear Second Order System",
International Journal of Engineering and
Manufacturing, vol.3, no.1, pp.38-57, 2013.DOI:
10.5815/ijem.2013.01.04.
Samaneh Zahmatkesh, Kamran Heidari, "Design
Artificial Intelligent Parallel Feedback Linearization
of PID Control with Application to Continuum
Robot", International Journal of Engineering and
Manufacturing, vol.3, no.2, pp.51-72, 2013.DOI:
10.5815/ijem.2013.02.04.
Mansour Bazregar, AliReza Nabaee,"Artificial
Algorithm: Applied in Continuum Robot
Manipulator", International Journal of Information
Engineering and Electronic Business, vol.5, no.5,
pp.57-69, 2013. DOI: 10.5815/ijieeb.2013.05.08.
Bamdad Boroomand, "Design Computed Torque
Controller with Parallel Fuzzy Inference System
Compensator to Control of Robot Manipulator",
International Journal of Information Engineering
and Electronic Business, vol.5, no.3, pp.66-77, 2013.
DOI: 10.5815/ijieeb.2013.03.08.
Omid reza Sadrnia, Omid Mahmoudi,"Design
Modified Fuzzy Hybrid Technique: Tuning By
GDO", IJMECS, vol.5, no.8, pp.58-72, 2013.DOI:
10.5815/ijmecs.2013.08.07.
Tannaz Khajeaian,Meysam Kazeminasab,"Design
IJMECS, vol.5, no.10, pp.53-63, 2013.DOI:
10.5815/ijmecs.2013.10.07.
Esmaeili, Mahdi Mirshekaran, Alireza
Dynamic Method plus Gravity Control for Highly
Nonlinear Continuum Robot", IJISA, vol.6, no.1,
pp.112-123, 2014. DOI: 10.5815/ijisa.2014.01.12.
Copyright © 2013 MECS I.J. Information Engineering and Electronic Business, 2013, 6, 22-32
[57] Mansour Bazregar, Farzin Piltan, Mehdi Akbari,
Mojdeh Piran,"Management of Automotive Engine
Based on Stable Fuzzy Technique with Parallel
Sliding Mode Optimization", IJITCS, vol.6, no.1,
pp.101-107, 2014. DOI: 10.5815/ijitcs.2014.01.12.
co researcher in Control and Robotic
Lab at the institute of advance science
and technology, SSP research and
development institute. His current
nonlinear control, artificial control
Iran. In 2004 he is jointed the research
and development company, SSP Co,
Shiraz, Iran. In addition to 7 textbooks,
Farzin Piltan is the main author of more
than 95 scientific papers in refereed
journals. He is supervised and managed
nonlinear control and artificial
intelligent Lab, Institute of Advance Science and
Technology for over 3 years. He also has highly skilled in
embeded system controller and device programming
including FPGA and CPLD. He is editorial review board
member for „international journal of control and
automation (IJCA), Australia, ISSN: 2005-4297;
„International Journal of Intelligent System and
Applications (IJISA), Hong Kong, ISSN:2074-9058;
„IAES international journal of robotics and automation,
Malaysia, ISSN:2089-4856; ‘International Journal of
Reconfigurable and Embedded Systems, Malaysia,
ISSN:2089-4864. His current research interests are
nonlinear control, artificial control system and applied to
FPGA, robotics and artificial nonlinear control and IC
engine modeling and control.
Samaneh Zahmatkesh is currently
advance science and technology, SSP
research and development institute. Her
current research interests are in the area
of nonlinear control, artificial control
system and robotics.
co researcher in Control and Robotic
Lab at the inistitute of advance science
and technology, SSP research and
development institute. Her current
nonlinear control, artificial control
co researcher in Control and Robotic
Lab at the institute of advance science
and technology, SSP research and
development institute. He is a Master in
field of Electrical Communication
current research interests are in the area of nonlinear
control, artificial control system, and robotics.